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Azriel Rosenfeld
Researcher at University of Maryland, College Park
Publications - 613
Citations - 50771
Azriel Rosenfeld is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Image processing & Feature detection (computer vision). The author has an hindex of 94, co-authored 595 publications receiving 49426 citations. Previous affiliations of Azriel Rosenfeld include Meiji University.
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A Comparative Study of Texture Measures for Terrain Classification
TL;DR: In this paper, three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively.
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Digital topology: introduction and survey
T. Y. Kong,Azriel Rosenfeld +1 more
TL;DR: The fundamental concepts of digital topology are reviewed and the major theoretical results in the field are surveyed, with a bibliography of almost 140 references.
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Distance functions on digital pictures
Azriel Rosenfeld,John L. Pfaltz +1 more
TL;DR: Algorithms for computing various functions on a digital picture which depend on the distance to a given subset of the picture, which involve local operations which are performed repeatedly, "in parallel”, on every picture element and its immediate neighbors are described.
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Edge and Curve Detection for Visual Scene Analysis
Azriel Rosenfeld,M. Thurston +1 more
TL;DR: Simple sets of parallel operations are described which can be used to detect texture edges, "spots," and "streaks" in digitized pictures and it is shown that a composite output is constructed in which edges between differently textured regions are detected, and isolated objects are also detected, but the objects composing the textures are ignored.
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Tracking Groups of People
TL;DR: A computer vision system for tracking multiple people in relatively unconstrained environments is described and should provide a useful mechanism for bootstrapping and reinitialization of tracking using more specific but less robust human models.